{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "to_train=pd.read_csv('input/train_time.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "      <th>college</th>\n",
       "      <th>rank</th>\n",
       "      <th>total_people</th>\n",
       "      <th>rank_percent</th>\n",
       "      <th>countM1</th>\n",
       "      <th>price_sumM1</th>\n",
       "      <th>price_avgM1</th>\n",
       "      <th>price_maxM1</th>\n",
       "      <th>...</th>\n",
       "      <th>地点263_avg</th>\n",
       "      <th>地点263_max</th>\n",
       "      <th>地点263_min</th>\n",
       "      <th>地点263_median</th>\n",
       "      <th>地点840_count</th>\n",
       "      <th>地点840_sum</th>\n",
       "      <th>地点840_avg</th>\n",
       "      <th>地点840_max</th>\n",
       "      <th>地点840_min</th>\n",
       "      <th>地点840_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2933.0</td>\n",
       "      <td>0.000341</td>\n",
       "      <td>49.0</td>\n",
       "      <td>201.31</td>\n",
       "      <td>4.108367</td>\n",
       "      <td>36.4</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2933.0</td>\n",
       "      <td>0.000682</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>6.157895</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>1570.0</td>\n",
       "      <td>0.996815</td>\n",
       "      <td>97.0</td>\n",
       "      <td>347.74</td>\n",
       "      <td>3.584948</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1570.0</td>\n",
       "      <td>1570.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>98.0</td>\n",
       "      <td>491.01</td>\n",
       "      <td>5.010306</td>\n",
       "      <td>17.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2304.0</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>27.0</td>\n",
       "      <td>82.96</td>\n",
       "      <td>3.072593</td>\n",
       "      <td>22.3</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 432 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label  college    rank  total_people  rank_percent  countM1  \\\n",
       "0   0    0.0      9.0     1.0        2933.0      0.000341     49.0   \n",
       "1   1    0.0      9.0     2.0        2933.0      0.000682     -1.0   \n",
       "2   8    0.0      6.0  1565.0        1570.0      0.996815     97.0   \n",
       "3   9    0.0      6.0  1570.0        1570.0      1.000000     98.0   \n",
       "4  10    0.0      3.0     1.0        2304.0      0.000434     27.0   \n",
       "\n",
       "   price_sumM1  price_avgM1  price_maxM1      ...       地点263_avg  地点263_max  \\\n",
       "0       201.31     4.108367         36.4      ...            -1.0       -1.0   \n",
       "1        -1.00    -1.000000         -1.0      ...            -1.0       -1.0   \n",
       "2       347.74     3.584948         10.0      ...            -1.0       -1.0   \n",
       "3       491.01     5.010306         17.5      ...            -1.0       -1.0   \n",
       "4        82.96     3.072593         22.3      ...            -1.0       -1.0   \n",
       "\n",
       "   地点263_min  地点263_median  地点840_count  地点840_sum  地点840_avg  地点840_max  \\\n",
       "0       -1.0          -1.0         -1.0       -1.0  -1.000000       -1.0   \n",
       "1       -1.0          -1.0         19.0      117.0   6.157895        7.0   \n",
       "2       -1.0          -1.0         -1.0       -1.0  -1.000000       -1.0   \n",
       "3       -1.0          -1.0         -1.0       -1.0  -1.000000       -1.0   \n",
       "4       -1.0          -1.0          2.0        7.0   3.500000        4.0   \n",
       "\n",
       "   地点840_min  地点840_median  \n",
       "0       -1.0          -1.0  \n",
       "1        4.0           6.0  \n",
       "2       -1.0          -1.0  \n",
       "3       -1.0          -1.0  \n",
       "4        3.0           3.5  \n",
       "\n",
       "[5 rows x 432 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0       9325\n",
       "1000.0     741\n",
       "1500.0     465\n",
       "2000.0     354\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_train['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f66204137d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MR1pVmCRp59XovMVtlPMdwCRgT+AnFIslSpLGmUaXZN9/6POIeAnFpbuSpHGo0QnzLWTm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\nkqTK/h/ZlyWOw5A5WAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66580d87d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_classes = pd.value_counts(to_train['label'], sort = True).sort_index()\n",
    "count_classes.plot(kind = 'bar')\n",
    "plt.title(\"Class histogram\")\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = to_train.ix[:, to_train.columns != 'label']\n",
    "y = to_train.ix[:, to_train.columns == 'label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "to_one = 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def to_01(x):\n",
    "    if x != 0:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kuhung/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "y['label']=y['label'].apply(lambda x : to_01(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Number transactions train dataset: ', 7619)\n",
      "('Number transactions test dataset: ', 3266)\n",
      "('Total number of transactions: ', 10885)\n"
     ]
    }
   ],
   "source": [
    "# 这里的采样方式，会造成数据分布变化 // resample to all data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Whole dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3, random_state = 42)\n",
    "\n",
    "print(\"Number transactions train dataset: \", len(X_train))\n",
    "print(\"Number transactions test dataset: \", len(X_test))\n",
    "print(\"Total number of transactions: \", len(X_train)+len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7619, 431)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kuhung/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "train=X_train\n",
    "train['label']=y_train\n",
    "\n",
    "train.to_csv('./input/train_binary.csv',index=False)\n",
    "train=pd.read_csv('./input/train_binary.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Number of normal transactions: ', 1081)\n",
      "('Number of fraud transactions: ', 1081)\n",
      "('Total number of transactions in resampled data: ', 2162)\n"
     ]
    }
   ],
   "source": [
    "number_records_fraud = len(train[train.label != 0])\n",
    "fraud_indices = np.array(train[train.label != 0].index)\n",
    "\n",
    "# Picking the indices of the normal classes\n",
    "normal_indices = train[train.label == 0].index\n",
    "\n",
    "# Out of the indices we picked, randomly select \"x\" number (number_records_fraud)\n",
    "random_normal_indices = np.random.choice(normal_indices, number_records_fraud, replace = False)\n",
    "random_normal_indices = np.array(random_normal_indices)\n",
    "\n",
    "# Appending the 2 indices\n",
    "under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])\n",
    "\n",
    "# Under sample dataset\n",
    "under_sample_data = train.iloc[under_sample_indices,:]\n",
    "\n",
    "X_undersample = under_sample_data.ix[:, under_sample_data.columns != 'label']\n",
    "y_undersample = under_sample_data.ix[:, under_sample_data.columns == 'label']\n",
    "\n",
    "# Showing ratio\n",
    "print(\"Number of normal transactions: \", len(under_sample_data[under_sample_data.label == 0]))\n",
    "print(\"Number of fraud transactions: \", len(under_sample_data[under_sample_data.label != 0]))\n",
    "print(\"Total number of transactions in resampled data: \", len(under_sample_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "('Number transactions train dataset: ', 1513)\n",
      "('Number transactions test dataset: ', 649)\n",
      "('Total number of transactions: ', 2162)\n"
     ]
    }
   ],
   "source": [
    "# Undersampled dataset\n",
    "X_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample = train_test_split(X_undersample\n",
    "                                                                                                   ,y_undersample\n",
    "                                                                                                   ,test_size = 0.3\n",
    "                                                                                                   ,random_state = 21)\n",
    "print(\"\")\n",
    "print(\"Number transactions train dataset: \", len(X_train_undersample))\n",
    "print(\"Number transactions test dataset: \", len(X_test_undersample))\n",
    "print(\"Total number of transactions: \", len(X_train_undersample)+len(X_test_undersample))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nice_feature=pd.read_csv('input/nice_feature.csv',header=None,index_col=0)\n",
    "\n",
    "target = 'label'\n",
    "IDcol = 'id'\n",
    "ids = X_test['id'].values\n",
    "\n",
    "all_feature = [x for x in train.columns if x not in [target]]\n",
    "predictors = [ x for x in all_feature if x in nice_feature.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kuhung/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import KFold, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix,precision_recall_curve,auc,roc_auc_score,roc_curve,recall_score,classification_report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def printing_Kfold_scores(x_train_data,y_train_data):\n",
    "    fold = KFold(len(y_train_data),5,shuffle=False) \n",
    "\n",
    "    # Different C parameters\n",
    "    c_param_range = [0.01,0.1,1,10,100]\n",
    "\n",
    "    results_table = pd.DataFrame(index = range(len(c_param_range),2), columns = ['C_parameter','Mean recall score'])\n",
    "    results_table['C_parameter'] = c_param_range\n",
    "\n",
    "    # the k-fold will give 2 lists: train_indices = indices[0], test_indices = indices[1]\n",
    "    j = 0\n",
    "    for c_param in c_param_range:\n",
    "        print('-------------------------------------------')\n",
    "        print('C parameter: ', c_param)\n",
    "        print('-------------------------------------------')\n",
    "        print('')\n",
    "\n",
    "        recall_accs = []\n",
    "        for iteration, indices in enumerate(fold,start=1):\n",
    "\n",
    "            # Call the logistic regression model with a certain C parameter\n",
    "            lr = LogisticRegression(C = c_param, penalty = 'l1')\n",
    "\n",
    "            # Use the training data to fit the model. In this case, we use the portion of the fold to train the model\n",
    "            # with indices[0]. We then predict on the portion assigned as the 'test cross validation' with indices[1]\n",
    "            lr.fit(x_train_data.iloc[indices[0],:],y_train_data.iloc[indices[0],:].values.ravel())\n",
    "\n",
    "            # Predict values using the test indices in the training data\n",
    "            y_pred_undersample = lr.predict(x_train_data.iloc[indices[1],:].values)\n",
    "\n",
    "            # Calculate the recall score and append it to a list for recall scores representing the current c_parameter\n",
    "            recall_acc = recall_score(y_train_data.iloc[indices[1],:].values,y_pred_undersample)\n",
    "            recall_accs.append(recall_acc)\n",
    "            print('Iteration ', iteration,': recall score = ', recall_acc)\n",
    "\n",
    "        # The mean value of those recall scores is the metric we want to save and get hold of.\n",
    "        results_table.ix[j,'Mean recall score'] = np.mean(recall_accs)\n",
    "        j += 1\n",
    "        print('')\n",
    "        print('Mean recall score ', np.mean(recall_accs))\n",
    "        print('')\n",
    "\n",
    "    best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']\n",
    "    \n",
    "    # Finally, we can check which C parameter is the best amongst the chosen.\n",
    "    print('*********************************************************************************')\n",
    "    print('Best model to choose from cross validation is with C parameter = ', best_c)\n",
    "    print('*********************************************************************************')\n",
    "    \n",
    "    return best_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "('C parameter: ', 0.01)\n",
      "-------------------------------------------\n",
      "\n",
      "('Iteration ', 1, ': recall score = ', 0.72368421052631582)\n",
      "('Iteration ', 2, ': recall score = ', 0.72435897435897434)\n",
      "('Iteration ', 3, ': recall score = ', 0.76973684210526316)\n",
      "('Iteration ', 4, ': recall score = ', 0.79432624113475181)\n",
      "('Iteration ', 5, ': recall score = ', 0.80519480519480524)\n",
      "\n",
      "('Mean recall score ', 0.76346021466402214)\n",
      "\n",
      "-------------------------------------------\n",
      "('C parameter: ', 0.1)\n",
      "-------------------------------------------\n",
      "\n",
      "('Iteration ', 1, ': recall score = ', 0.80263157894736847)\n",
      "('Iteration ', 2, ': recall score = ', 0.86538461538461542)\n",
      "('Iteration ', 3, ': recall score = ', 0.85526315789473684)\n",
      "('Iteration ', 4, ': recall score = ', 0.86524822695035464)\n",
      "('Iteration ', 5, ': recall score = ', 0.85064935064935066)\n",
      "\n",
      "('Mean recall score ', 0.84783538596528518)\n",
      "\n",
      "-------------------------------------------\n",
      "('C parameter: ', 1)\n",
      "-------------------------------------------\n",
      "\n",
      "('Iteration ', 1, ': recall score = ', 0.82236842105263153)\n",
      "('Iteration ', 2, ': recall score = ', 0.85897435897435892)\n",
      "('Iteration ', 3, ': recall score = ', 0.86184210526315785)\n",
      "('Iteration ', 4, ': recall score = ', 0.86524822695035464)\n",
      "('Iteration ', 5, ': recall score = ', 0.8571428571428571)\n",
      "\n",
      "('Mean recall score ', 0.85311519387667212)\n",
      "\n",
      "-------------------------------------------\n",
      "('C parameter: ', 10)\n",
      "-------------------------------------------\n",
      "\n",
      "('Iteration ', 1, ': recall score = ', 0.81578947368421051)\n",
      "('Iteration ', 2, ': recall score = ', 0.86538461538461542)\n",
      "('Iteration ', 3, ': recall score = ', 0.86184210526315785)\n",
      "('Iteration ', 4, ': recall score = ', 0.86524822695035464)\n",
      "('Iteration ', 5, ': recall score = ', 0.8571428571428571)\n",
      "\n",
      "('Mean recall score ', 0.85308145568503913)\n",
      "\n",
      "-------------------------------------------\n",
      "('C parameter: ', 100)\n",
      "-------------------------------------------\n",
      "\n",
      "('Iteration ', 1, ': recall score = ', 0.81578947368421051)\n",
      "('Iteration ', 2, ': recall score = ', 0.86538461538461542)\n",
      "('Iteration ', 3, ': recall score = ', 0.86184210526315785)\n",
      "('Iteration ', 4, ': recall score = ', 0.85815602836879434)\n",
      "('Iteration ', 5, ': recall score = ', 0.8571428571428571)\n",
      "\n",
      "('Mean recall score ', 0.85166301596872707)\n",
      "\n",
      "*********************************************************************************\n",
      "('Best model to choose from cross validation is with C parameter = ', 1.0)\n",
      "*********************************************************************************\n"
     ]
    }
   ],
   "source": [
    "best_c = printing_Kfold_scores(X_train_undersample[predictors],y_train_undersample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=0)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        #print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        1#print('Confusion matrix, without normalization')\n",
    "\n",
    "    #print(cm)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.877300613497\n",
      "F1: 0.802244039271\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZCx3miaSOwrPpzQNGAJjZIcBKd99Y3i5JCeiIopnTkHRlYGY3A4OB7cA4d3+p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\nFBGJQOEpIhLB/wOCBNg47mlChwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66179f5b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use this C_parameter to build the final model with the whole training dataset and predict the classes in the test\n",
    "# dataset\n",
    "best_c = 1\n",
    "lr = LogisticRegression(C = best_c, penalty = 'l1')\n",
    "lr.fit(X_train_undersample[predictors],y_train_undersample.values.ravel())\n",
    "y_pred_undersample = lr.predict(X_test_undersample[predictors])\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test_undersample,y_pred_undersample)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print(\"Recall: %s\" %(float(cnf_matrix[1,1])/(cnf_matrix[1,0]+cnf_matrix[1,1])))\n",
    "print(\"F1: %s\" %(float(2*cnf_matrix[1,1])/(cnf_matrix[1,0]+2*cnf_matrix[1,1]+cnf_matrix[0,1])))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion matrix')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.872651356994\n",
      "F1: 0.456082924168\n"
     ]
    },
    {
     "data": {
      "image/png": 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+MnQ58FG4PQ2Y5e4jgaXA5HCa0suB0QRj6y80s865tF+hKiLxEnGqWjBN3n4E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\nTK7xG6Z1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66179db750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use this C_parameter to build the final model with the whole training dataset and predict the classes in the test\n",
    "# dataset\n",
    "lr = LogisticRegression(C = best_c, penalty = 'l1')\n",
    "lr.fit(X_train_undersample[predictors],y_train_undersample.values.ravel())\n",
    "y_pred = lr.predict(X_test[predictors])\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print(\"Recall: %s\" %(float(cnf_matrix[1,1])/(cnf_matrix[1,0]+cnf_matrix[1,1])))\n",
    "print(\"F1: %s\" %(float(2*cnf_matrix[1,1])/(cnf_matrix[1,0]+2*cnf_matrix[1,1]+cnf_matrix[0,1])))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4RUS6Ah8CFwP3eR2YSYxd2trKamroOGEshUMGkjX/U2p335NAtb3/pn1KpH1i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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66179ed150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC CURVE\n",
    "lr = LogisticRegression(C = best_c, penalty = 'l1')\n",
    "y_pred_undersample_score = lr.fit(X_train_undersample[predictors],y_train_undersample.values.ravel()).decision_function(X_test_undersample[predictors])\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test_undersample.values.ravel(),y_pred_undersample_score)\n",
    "roc_auc = auc(fpr,tpr)\n",
    "\n",
    "# Plot ROC\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b',label='AUC = %0.2f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.1,1.0])\n",
    "plt.ylim([-0.1,1.01])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "('C parameter: ', 0.01)\n",
      "-------------------------------------------\n",
      "\n",
      "('Iteration ', 1, ': recall score = ', 0.080357142857142863)\n",
      "('Iteration ', 2, ': recall score = ', 0.058035714285714288)\n",
      "('Iteration ', 3, ': recall score = ', 0.061320754716981132)\n",
      "('Iteration ', 4, ': recall score = ', 0.071770334928229665)\n",
      "('Iteration ', 5, ': recall score = ', 0.056603773584905662)\n",
      "\n",
      "('Mean recall score ', 0.065617544074594719)\n",
      "\n",
      "-------------------------------------------\n",
      "('C parameter: ', 0.1)\n",
      "-------------------------------------------\n",
      "\n",
      "('Iteration ', 1, ': recall score = ', 0.084821428571428575)\n",
      "('Iteration ', 2, ': recall score = ', 0.0625)\n",
      "('Iteration ', 3, ': recall score = ', 0.061320754716981132)\n",
      "('Iteration ', 4, ': recall score = ', 0.066985645933014357)\n",
      "('Iteration ', 5, ': recall score = ', 0.066037735849056603)\n",
      "\n",
      "('Mean recall score ', 0.068333113014096142)\n",
      "\n",
      "-------------------------------------------\n",
      "('C parameter: ', 1)\n",
      "-------------------------------------------\n",
      "\n",
      "('Iteration ', 1, ': recall score = ', 0.080357142857142863)\n",
      "('Iteration ', 2, ': recall score = ', 0.053571428571428568)\n",
      "('Iteration ', 3, ': recall score = ', 0.066037735849056603)\n",
      "('Iteration ', 4, ': recall score = ', 0.057416267942583733)\n",
      "('Iteration ', 5, ': recall score = ', 0.051886792452830191)\n",
      "\n",
      "('Mean recall score ', 0.061853873534608396)\n",
      "\n",
      "-------------------------------------------\n",
      "('C parameter: ', 10)\n",
      "-------------------------------------------\n",
      "\n",
      "('Iteration ', 1, ': recall score = ', 0.080357142857142863)\n",
      "('Iteration ', 2, ': recall score = ', 0.053571428571428568)\n",
      "('Iteration ', 3, ': recall score = ', 0.061320754716981132)\n",
      "('Iteration ', 4, ': recall score = ', 0.057416267942583733)\n",
      "('Iteration ', 5, ': recall score = ', 0.042452830188679243)\n",
      "\n",
      "('Mean recall score ', 0.059023684855363114)\n",
      "\n",
      "-------------------------------------------\n",
      "('C parameter: ', 100)\n",
      "-------------------------------------------\n",
      "\n",
      "('Iteration ', 1, ': recall score = ', 0.080357142857142863)\n",
      "('Iteration ', 2, ': recall score = ', 0.053571428571428568)\n",
      "('Iteration ', 3, ': recall score = ', 0.061320754716981132)\n",
      "('Iteration ', 4, ': recall score = ', 0.052631578947368418)\n",
      "('Iteration ', 5, ': recall score = ', 0.042452830188679243)\n",
      "\n",
      "('Mean recall score ', 0.058066747056320048)\n",
      "\n",
      "*********************************************************************************\n",
      "('Best model to choose from cross validation is with C parameter = ', 0.10000000000000001)\n",
      "*********************************************************************************\n"
     ]
    }
   ],
   "source": [
    "best_c = printing_Kfold_scores(X_train[predictors],y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.0584551148225\n",
      "F1: 0.100358422939\n"
     ]
    },
    {
     "data": {
      "image/png": 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wBngDWOTuqwHMbD5wAPCXfBtUqIpIosR8+L890MbMHgQ6AJcB5e6+Plr/CdAN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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f661749ec10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use this C_parameter to build the final model with the whole training dataset and predict the classes in the test\n",
    "# dataset\n",
    "lr = LogisticRegression(C = best_c, penalty = 'l1')\n",
    "lr.fit(X_train[predictors],y_train.values.ravel())\n",
    "y_pred_undersample = lr.predict(X_test[predictors])\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pred_undersample)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print(\"Recall: %s\" %(float(cnf_matrix[1,1])/(cnf_matrix[1,0]+cnf_matrix[1,1])))\n",
    "print(\"F1: %s\" %(float(2*cnf_matrix[1,1])/(cnf_matrix[1,0]+2*cnf_matrix[1,1]+cnf_matrix[0,1])))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion matrix')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n## resample to resample\\n\\nlr = LogisticRegression(C = 1, penalty = \\'l1\\')\\nlr.fit(X_train_undersample[predictors],y_train_undersample.values.ravel())\\ny_pred_undersample_proba = lr.predict_proba(X_test_undersample[predictors])\\n\\n\\nthresholds = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\\n\\nplt.figure(figsize=(10,10))\\n\\nj = 1\\nfor i in thresholds:\\n    y_test_predictions_high_recall = y_pred_undersample_proba[:,1] > i\\n    \\n    plt.subplot(3,3,j)\\n    j += 1\\n    \\n    # Compute confusion matrix\\n    cnf_matrix = confusion_matrix(y_test_undersample,y_test_predictions_high_recall)\\n    np.set_printoptions(precision=2)\\n    \\n    print(\\'thresholds %s\\' %i)\\n    print(\"Recall %s\" %(float(cnf_matrix[1,1])/(cnf_matrix[1,0]+cnf_matrix[1,1]))),\\n    print(\"F1 %s\" %(float(2*cnf_matrix[1,1])/(cnf_matrix[1,0]+2*cnf_matrix[1,1]+cnf_matrix[0,1])))\\n    print(\\' \\')\\n\\n    # Plot non-normalized confusion matrix\\n    class_names = [0,1]\\n    plot_confusion_matrix(cnf_matrix\\n                          , classes=class_names\\n                          , title=\\'Threshold >= %s\\'%i) \\n\\n'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "## resample to resample\n",
    "\n",
    "lr = LogisticRegression(C = 1, penalty = 'l1')\n",
    "lr.fit(X_train_undersample[predictors],y_train_undersample.values.ravel())\n",
    "y_pred_undersample_proba = lr.predict_proba(X_test_undersample[predictors])\n",
    "\n",
    "\n",
    "thresholds = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "j = 1\n",
    "for i in thresholds:\n",
    "    y_test_predictions_high_recall = y_pred_undersample_proba[:,1] > i\n",
    "    \n",
    "    plt.subplot(3,3,j)\n",
    "    j += 1\n",
    "    \n",
    "    # Compute confusion matrix\n",
    "    cnf_matrix = confusion_matrix(y_test_undersample,y_test_predictions_high_recall)\n",
    "    np.set_printoptions(precision=2)\n",
    "    \n",
    "    print('thresholds %s' %i)\n",
    "    print(\"Recall %s\" %(float(cnf_matrix[1,1])/(cnf_matrix[1,0]+cnf_matrix[1,1]))),\n",
    "    print(\"F1 %s\" %(float(2*cnf_matrix[1,1])/(cnf_matrix[1,0]+2*cnf_matrix[1,1]+cnf_matrix[0,1])))\n",
    "    print(' ')\n",
    "\n",
    "    # Plot non-normalized confusion matrix\n",
    "    class_names = [0,1]\n",
    "    plot_confusion_matrix(cnf_matrix\n",
    "                          , classes=class_names\n",
    "                          , title='Threshold >= %s'%i) \n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "thresholds 0.1\n",
      "Recall 0.991649269311 Precision 0.228585178056 F1 0.371529135706\n",
      "\n",
      "thresholds 0.2\n",
      "Recall 0.979123173278 Precision 0.268153230417 F1 0.421005385996\n",
      "\n",
      "thresholds 0.24\n",
      "Recall 0.972860125261 Precision 0.273795534665 F1 0.42732691426\n",
      "\n",
      "thresholds 0.26\n",
      "Recall 0.972860125261 Precision 0.275902901125 F1 0.429889298893\n",
      "\n",
      "thresholds 0.28\n",
      "Recall 0.970772442589 Precision 0.278443113772 F1 0.432759422987\n",
      "\n",
      "thresholds 0.3\n",
      "Recall 0.966597077244 Precision 0.280266343826 F1 0.434537775692\n",
      "\n",
      "thresholds 0.32\n",
      "Recall 0.964509394572 Precision 0.284833538841 F1 0.439790575916\n",
      "\n",
      "thresholds 0.34\n",
      "Recall 0.958246346555 Precision 0.285981308411 F1 0.440499040307\n",
      "\n",
      "thresholds 0.36\n",
      "Recall 0.947807933194 Precision 0.287888395688 F1 0.441634241245\n",
      "\n",
      "thresholds 0.38\n",
      "Recall 0.947807933194 Precision 0.290839205637 F1 0.445098039216\n",
      "\n",
      "thresholds 0.4\n",
      "Recall 0.935281837161 Precision 0.293193717277 F1 0.446437468859\n",
      "\n",
      "thresholds 0.46\n",
      "Recall 0.910229645094 Precision 0.304044630404 F1 0.455828541558\n",
      "\n",
      "thresholds 0.48\n",
      "Recall 0.889352818372 Precision 0.306474820144 F1 0.455858747994\n",
      "\n",
      "thresholds 0.5\n",
      "Recall 0.872651356994 Precision 0.308714918759 F1 0.456082924168\n",
      "\n",
      "thresholds 0.52\n",
      "Recall 0.86012526096 Precision 0.31474407945 F1 0.460850111857\n",
      "\n",
      "thresholds 0.54\n",
      "Recall 0.845511482255 Precision 0.317398119122 F1 0.461538461538\n",
      "\n",
      "thresholds 0.56\n",
      "Recall 0.835073068894 Precision 0.32154340836 F1 0.464306442252\n",
      "\n",
      "thresholds 0.58\n",
      "Recall 0.816283924843 Precision 0.325291181364 F1 0.465199286139\n",
      "\n",
      "thresholds 0.6\n",
      "Recall 0.778705636743 Precision 0.324912891986 F1 0.458512599877\n",
      "\n",
      "thresholds 0.62\n",
      "Recall 0.759916492693 Precision 0.333944954128 F1 0.463989802422\n",
      "\n",
      "thresholds 0.64\n",
      "Recall 0.716075156576 Precision 0.332364341085 F1 0.45400397088\n",
      "\n",
      "thresholds 0.66\n",
      "Recall 0.674321503132 Precision 0.334368530021 F1 0.447058823529\n",
      "\n",
      "thresholds 0.7\n",
      "Recall 0.580375782881 Precision 0.334536702768 F1 0.424427480916\n",
      "\n",
      "thresholds 0.8\n",
      "Recall 0.313152400835 Precision 0.337837837838 F1 0.32502708559\n",
      "\n",
      "thresholds 0.9\n",
      "Recall 0.0772442588727 Precision 0.377551020408 F1 0.128249566724\n"
     ]
    },
    {
     "data": {
      "image/png": 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58dc1G7Zd8tj7vDQ9mAH80+/5NKlfB4DaNfOoVSOPrWrXoLAozpqC9RzYtSUT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MoAkbClPQOqyt0AkeYik8nva0XMrBtRQXAHYCDwM+AS4AZ33wV4DxhqZu2B84HdgH7AmWbWtTjv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KEyQCQDUWdAHag7qgwQyYD6ASLJpgwQSbZ8ZIC7Vz7ZP5ngEv8HgT6VtrcFXgYWksFVRFUO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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66179bbed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## resample to all data\n",
    "\n",
    "lr = LogisticRegression(C = 1, penalty = 'l1')\n",
    "lr.fit(X_train_undersample[predictors],y_train_undersample.values.ravel())\n",
    "y_pred_undersample_proba = lr.predict_proba(X_test[predictors])\n",
    "\n",
    "\n",
    "thresholds = [0.1,0.2,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.7,0.8,0.9]\n",
    "\n",
    "plt.figure(figsize=(18,18))\n",
    "\n",
    "j = 1\n",
    "for i in thresholds:\n",
    "    y_test_predictions_high_recall = y_pred_undersample_proba[:,1] > i\n",
    "    \n",
    "    plt.subplot(5,5,j)\n",
    "    j += 1\n",
    "    \n",
    "    # Compute confusion matrix\n",
    "    cnf_matrix = confusion_matrix(y_test,y_test_predictions_high_recall)\n",
    "    np.set_printoptions(precision=2)\n",
    "    \n",
    "    print('\\nthresholds %s' %i)\n",
    "    print(\"Recall %s\" %(float(cnf_matrix[1,1])/(cnf_matrix[1,0]+cnf_matrix[1,1]))),\n",
    "    print(\"Precision %s\" %(float(cnf_matrix[1,1])/(cnf_matrix[0,1]+cnf_matrix[1,1]))),\n",
    "    print(\"F1 %s\" %(float(2*cnf_matrix[1,1])/(cnf_matrix[1,0]+2*cnf_matrix[1,1]+cnf_matrix[0,1])))\n",
    "\n",
    "    # Plot non-normalized confusion matrix\n",
    "    class_names = [0,1]\n",
    "    plot_confusion_matrix(cnf_matrix\n",
    "                          , classes=class_names\n",
    "                          , title='Threshold >= %s'%i) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n## no resample to all data\\n\\nlr = LogisticRegression(C = 0.1, penalty = \\'l1\\')\\nlr.fit(X_train[predictors],y_train.values.ravel())\\ny_pred_undersample_proba = lr.predict_proba(X_test[predictors])\\n\\n\\nthresholds = [0.1,0.12,0.14,0.16,0.18,0.2,0.22,0.24,0.26,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\\n\\nplt.figure(figsize=(10,10))\\n\\nj = 1\\nfor i in thresholds:\\n    y_test_predictions_high_recall = y_pred_undersample_proba[:,1] > i\\n    \\n    plt.subplot(4,4,j)\\n    j += 1\\n    \\n    # Compute confusion matrix\\n    cnf_matrix = confusion_matrix(y_test,y_test_predictions_high_recall)\\n    np.set_printoptions(precision=2)\\n    \\n    print(\\'thresholds %s\\' %i)\\n    print(\"Recall %s\" %(float(cnf_matrix[1,1])/(cnf_matrix[1,0]+cnf_matrix[1,1]))),\\n    print(\"Precision %s\" %(float(cnf_matrix[1,1])/(cnf_matrix[0,1]+cnf_matrix[1,1]))),\\n    print(\"F1 %s\" %(float(2*cnf_matrix[1,1])/(cnf_matrix[1,0]+2*cnf_matrix[1,1]+cnf_matrix[0,1])))\\n    print(\\' \\')\\n\\n    # Plot non-normalized confusion matrix\\n    class_names = [0,1]\\n    plot_confusion_matrix(cnf_matrix\\n                          , classes=class_names\\n                          , title=\\'Threshold >= %s\\'%i) \\n\\n'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "## no resample to all data\n",
    "\n",
    "lr = LogisticRegression(C = 0.1, penalty = 'l1')\n",
    "lr.fit(X_train[predictors],y_train.values.ravel())\n",
    "y_pred_undersample_proba = lr.predict_proba(X_test[predictors])\n",
    "\n",
    "\n",
    "thresholds = [0.1,0.12,0.14,0.16,0.18,0.2,0.22,0.24,0.26,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "j = 1\n",
    "for i in thresholds:\n",
    "    y_test_predictions_high_recall = y_pred_undersample_proba[:,1] > i\n",
    "    \n",
    "    plt.subplot(4,4,j)\n",
    "    j += 1\n",
    "    \n",
    "    # Compute confusion matrix\n",
    "    cnf_matrix = confusion_matrix(y_test,y_test_predictions_high_recall)\n",
    "    np.set_printoptions(precision=2)\n",
    "    \n",
    "    print('thresholds %s' %i)\n",
    "    print(\"Recall %s\" %(float(cnf_matrix[1,1])/(cnf_matrix[1,0]+cnf_matrix[1,1]))),\n",
    "    print(\"Precision %s\" %(float(cnf_matrix[1,1])/(cnf_matrix[0,1]+cnf_matrix[1,1]))),\n",
    "    print(\"F1 %s\" %(float(2*cnf_matrix[1,1])/(cnf_matrix[1,0]+2*cnf_matrix[1,1]+cnf_matrix[0,1])))\n",
    "    print(' ')\n",
    "\n",
    "    # Plot non-normalized confusion matrix\n",
    "    class_names = [0,1]\n",
    "    plot_confusion_matrix(cnf_matrix\n",
    "                          , classes=class_names\n",
    "                          , title='Threshold >= %s'%i) \n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "三个想法：\n",
    "\n",
    "- 后threshold。多分类后再与threshold取交集。\n",
    "- 将二分类结果（概率）作为新特征\n",
    "- 先threshold。threshold改变数据分布后再多分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "best_c=0.1\n",
    "\n",
    "to_test=pd.read_csv('input/test_time.csv')\n",
    "to_test.drop(['label'],axis=1,inplace=True)\n",
    "\n",
    "\n",
    "lr = LogisticRegression(C = best_c, penalty = 'l1')\n",
    "lr.fit(X_undersample[predictors],y_undersample[target])\n",
    "y_pred_undersample_proba = lr.predict_proba(to_test[predictors])\n",
    "\n",
    "\n",
    "y_test_predictions_high_recall = y_pred_undersample_proba[:,1] > 0.54\n",
    "\n",
    "to_test['predict']=y_test_predictions_high_recall\n",
    "\n",
    "def to_score(x):\n",
    "    if x ==True:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "to_test['predict']=to_test['predict'].apply(lambda x:to_score(x))\n",
    "\n",
    "out=pd.DataFrame()\n",
    "out['id']=to_test['id']\n",
    "out['predict']=to_test['predict']\n",
    "\n",
    "test=pd.read_csv('./test/studentID_test.csv',header=None)\n",
    "test.columns=['id']\n",
    "\n",
    "out=test.merge(out,on='id',how='outer').fillna(0).astype(int)\n",
    "out.columns=['studentid','judge']\n",
    "\n",
    "#out.to_csv('../sub_lr_1.15.csv',index=False)\n",
    "out.to_csv('output/threshold-plus.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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